A swarm-based volition/attention framework for object recognition

IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops

Volumn 2005-September, Issue , 2005, Pages

  Owechko, Yuri ^a   Medasani, Swarup ^a  

^a Hughes Aircraft Co   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BEHAVIORAL RESEARCH; COMPUTER VISION; INTELLIGENT AGENTS; OBJECT DETECTION; PARTICLE SWARM OPTIMIZATION (PSO);

BOTTOM-UP ATTENTION; EVOLUTIONARY SEARCH; INDEPENDENT CLASSIFIERS; NOVEL ARCHITECTURE; OBJECT CLASSIFICATION; OBJECT POSITIONS; SPATIALLY MODULATED; VISUAL ATTENTION;

OBJECT RECOGNITION;

EID: 77953577908     PISSN: 21607508     EISSN: 21607516     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2005.397     Document Type: Conference Paper

References (20)

1. J. Kennedy and R. Eberhart, “Particle Swarm Optimization,” IEEE Inter. Conference on Neural Networks, 1995.
2. D. L. Swets, B. Punch, and J. Weng, “Genetic Algorithms for Object Recognition in a complex scene,” Proc. of Intl. Conference on Image Processing, vol. 2, Oct, pp 23-26, 1995.
3. V. Ciesielski and M. Zhang, “Using Genetic Algorithms to Improve the Accuracy of Object Detection,” In Proceedings of the third Pacific-Asia Knowledge Discovery and Data Mining Conference, Tsinghua University Press, Beijing, April 26-31, 1999.
4. J. Kennedy, R. C. Eberhart, and Y. Shi, Swarm Intelligence San Francisco: Morgan Kaufmann Publishers, 2001.
5. R. C. Eberhart and Y. Shi, “Particle Swarm Optimization: Developments, Applications, and Resources,” 2001.
6. R. Brits, A. P. Engelbrecht, and F. van den Bergh, “A Niching Particle Swarm Optimizer,” 2002.
7. G. Deco and B. Schurmann. A hierarchical neural system with attentional top-down enhancement of the spatial resolution for object recognition. Vision Res., 40(20):2845-2859, 2000.
8. L. Itti, C. Koch, and E. Niebur. A model of saliency based visual attention for rapid scene analysis. IEEE PAMI, 20(11):1254-1259, 1998.
9. J. K. Tsotsos, S. M. Culhane, W. Y. K. Wai, Y. H. Lai, N. Davis, and F. Nuflo. Modeling visual-attention via selective tuning. Artificial Intelligence, 78:507-545, 1995.
10. C. Koch and S. Ullman. Shifts in selective visual attention: towards the underlying neural circuitry. Human Neurobiology, 4:219-227, 1985.
11. U. Neisser. Cognitive Psychology. Appleton-Century-Crofts, New York, 1967.
12. A. Treisman and G.Gelade. A feature integration theory of attention. Cognitive Psychology, 12:97-136, 1980.
13. J.M. Wolfe. Guided search 2.0: A revised model of visual search. Psychonomic Bulletin and Review, 1(2):202-238, 1994.
14. A.L. Yarbus. Eye Movements and Vision. Plenum Press, New York, 1967.
15. V. Navalpakkam and L. Itti. Modeling the influence of task on attention. Vision Research, Vol. 45(2), pp. 205-231, 2005.
16. D. Walther, U. Rutishauser, C. Koch, and P. Perona. On the usefulness of attention for object recognition. 2nd Workshop on Attention and Performance in Computational Vision at the European Conference for Computer Vision (2004), 96-103.
17. F. Miau, C. Papageorgiou, and L. Itti. Neuromorphic algorithms for computer vision and attention. In Proc. SPIE 46 Annual International Symposium on Optical Science and Technology, volume 4479, pages 12-23, Nov 2001.
18. S. Frintrop, A. Nuchter, H. Surmann. Visual attention for object recognition in spatial 3D data. 2nd Workshop on Attention and Performance in Computational Vision at the European Conference for Computer Vision (2004).
19. F. H. Hamker and J. Worcester, “Object detection in natural scenes by feedback,” In H. H. Buelthoff et al. (eds.), Biologically Motivated Computer Vision. Lecture notes in comp. science. Springer Verlag, 398-407, 2002.
20. F. H. Hamker, “The Reentry Hypothesis: The putative interaction of the frontal eye field, ventrolateral prefrontal cortex, and areas v4, IT for attention and eye movement,” Cerebral Cortex, 15:431-447, 2005.